Data Science & AI
Complete Machine Learning Mastery
Master the complete data science pipeline from data collection to model deployment, including machine learning, deep learning, and MLOps with real-world projects.
Your Data Science Journey
Data Analysis
Master Python, statistics, and data visualization
Machine Learning
Build predictive models with advanced algorithms
MLOps & Deployment
Deploy models at scale with cloud platforms
Detailed Learning Roadmap
Python for Data Science & Statistics Fundamentals
BeginnerLearning Topics
- Python basics: variables, data types, control structures
- NumPy for numerical computing and arrays
- Pandas for data manipulation and analysis
- Matplotlib and Seaborn for data visualization
- Descriptive statistics: mean, median, mode, variance
- Probability distributions and hypothesis testing
- Correlation, covariance, and statistical significance
- A/B testing and experimental design
Hands-on Project
Analyze a real-world dataset to uncover insights about sales patterns, customer behavior, and market trends.
Data Collection, Cleaning & Exploratory Analysis
IntermediateLearning Topics
- Web scraping with BeautifulSoup and Scrapy
- Working with REST APIs and JSON data
- Database connections: SQL and NoSQL
- Data collection ethics and best practices
- Handling missing data and outliers
- Data type conversions and standardization
- Feature engineering and transformation
- Data quality assessment and validation
Hands-on Project
Build a comprehensive data pipeline that collects, cleans, and prepares e-commerce data for analysis.
Machine Learning Fundamentals
IntermediateLearning Topics
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines (SVM)
- Model evaluation and cross-validation
- K-means and hierarchical clustering
- Principal Component Analysis (PCA)
- Association rules and market basket analysis
- Anomaly detection techniques
Hands-on Project
Develop a customer segmentation model for an e-commerce platform using clustering algorithms.
Advanced Machine Learning & Deep Learning
AdvancedLearning Topics
- Ensemble methods: bagging, boosting, stacking
- XGBoost, LightGBM, and CatBoost
- Hyperparameter tuning with GridSearch and Optuna
- Feature selection and dimensionality reduction
- Neural networks and backpropagation
- TensorFlow and Keras for deep learning
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
Hands-on Project
Build an image classification system using CNNs and a time series forecasting model using LSTMs.
Big Data Technologies & Cloud Platforms
AdvancedLearning Topics
- Apache Spark for distributed computing
- PySpark for big data processing with Python
- Hadoop ecosystem and HDFS
- Stream processing with Kafka
- AWS services: S3, EC2, SageMaker
- Google Cloud Platform: BigQuery, AI Platform
- Azure Machine Learning Studio
- Docker and Kubernetes for ML deployment
Hands-on Project
Process large-scale datasets using Spark and deploy ML models on cloud platforms.
MLOps & Model Deployment
AdvancedLearning Topics
- Flask and FastAPI for model serving
- RESTful APIs for machine learning models
- Model versioning and experiment tracking
- A/B testing for ML models in production
- MLflow for experiment tracking and model registry
- CI/CD pipelines for machine learning
- Model monitoring and drift detection
- Automated retraining and model updates
Hands-on Project
Deploy a complete ML pipeline with automated training, testing, and deployment using MLOps practices.
Specialized Domains & Capstone Project
AdvancedLearning Topics
- Text preprocessing and tokenization
- Sentiment analysis and text classification
- Named Entity Recognition (NER)
- Transformers and BERT models
- Image preprocessing and augmentation
- Object detection with YOLO
- Transfer learning with pre-trained models
- GANs and image generation
Hands-on Project
Complete capstone project: Build an end-to-end AI application combining NLP, computer vision, and deployment.
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Join the AI revolution with our comprehensive data science program designed for the modern industry.